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How to cite this paper
APA: Duan, G & Song, G. (2025). Corporate marketing based on improved depth-weighted k-mean arithmetic and improved extreme gradient boosting tree. International Journal of Industrial Engineering Computations, 16(4), 1143-1154.
Chicago/Turabian: Duan, G & Song, G. 2025. "Corporate marketing based on improved depth-weighted k-mean arithmetic and improved extreme gradient boosting tree." International Journal of Industrial Engineering Computations 16, no. 4 (2025): 1143-1154.
AMA: Duan, G & Song, G. Corporate marketing based on improved depth-weighted k-mean arithmetic and improved extreme gradient boosting tree. International Journal of Industrial Engineering Computations. 2025;16(4):1143-1154.
References
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Naami, T., Anesbury, Z. W., Stocchi, L., & Winchester, M. (2022). How websites compete in the Middle East: The example of Iran. Journal of Consumer Behaviour, 21(1), 121-136.
Preethi, P., & Mamatha, H. R. (2023). Region-based convolutional neural network for segmenting text in epigraphical images. Artificial Intelligence and Applications, 1(2), 119-127.
Qin, H., Zhou, H., & Cao, J. (2020). Imbalanced learning algorithm based intelligent abnormal electricity consumption detection. Neurocomputing, 402, 112-123.
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Singh, P. P., Anik, F. I., Senapati, R., Sinha, A., Sakib, N., & Hossain, E. (2024). Investigating customer churn in banking: A machine learning approach and visualization app for data science and management. Data Science and Management, 7(1), 7-16.
Tao, T., Liu, Y., Qiao, Y., Gao, L., Lu, J., Zhang, C., & Wang, Y. (2021). Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm. Renewable Energy, 180, 1004-1013.
Wang, K., Lu, J., Liu, A., Song, Y., Xiong, L., & Zhang, G. (2022). Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. Neurocomputing, 491(6), 288-304.
Xian, H., & Che, J. (2021). A variable weight combined model based on time similarity and particle swarm optimization for short-term power load forecasting. IAENG International Journal of Computer Science, 48(4), 915-924.
Yigit, A. T., Samak, B., & Kaya, T. (2020). An XGBoost-lasso ensemble modeling approach to football player value assessment. Journal of Intelligent & Fuzzy Systems, 39(5), 6303-6314.
Zhao, X., Nie, F., Wang, R., & Li, X. (2022). Improving projected fuzzy K-means clustering via robust learning. Neurocomputing, 491(6), 34-43.
Zhou, B., & Gu, X. (2020). Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection. Neurocomputing, 376(2), 222-231.
Dickinson, T. A., Richman, M. B., & Furtado, J. C. (2021). Subseasonal-to-seasonal extreme precipitation events in the contiguous United States: Generation of a database and climatology. Journal of Climate, 34(18), 7571-7586.
Ehsani, F., & Hosseini, M. (2024). Customer churn prediction using a novel meta-classifier: an investigation on transaction, Telecommunication and customer churn datasets. Journal of Combinatorial Optimization, 48(1), 7.
Farman, H., Khan, A. W., Ahmed, S., Khan, D., Imran, M., & Bajaj, P. (2024). An analysis of supervised machine learning techniques for churn forecasting and component identification in the telecom sector. Journal of Computing & Biomedical Informatics, 7(01), 264-280.
Ghaffari, R., Golpardaz, M., Helfroush, M. S., & Danyali, H. (2020). A fast, weighted CRF algorithm based on a two-step superpixel generation for SAR image segmentation. International Journal of Remote Sensing, 41(9), 3535-3557.
Gurung, N., Hasan, M. R., Gazi, M. S., & Chowdhury, F. R. (2024). AI-based customer churn prediction model for business markets in the usa: exploring the use of ai and machine learning technologies in preventing customer churn. Journal of Computer Science and Technology Studies, 6(2), 19-29.
Kumar, D. M., Satyanarayana, D., & Prasad, M. G. (2021). An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation. Multimedia Tools and Applications, 80(5), 6939-6957.
Hak Lee, E., Kim, K., Kho, S. Y., Kim, D. K., & Cho, S. H. (2021). Estimating express train preference of urban railway passengers based on extreme gradient boosting (XGBoost) using smart card data. Transportation Research Record, 2675(11), 64-76.
Li, C., Zhang, Y., Cui, C., Fan, D., Zhao, Y., Wu, X. B., ... & Yang, S. (2021). Identification of BASS DR3 sources as stars, galaxies, and quasars by XGBoost. Monthly Notices of the Royal Astronomical Society, 506(2), 1651-1664.
Mano, A., & Anand, S. (2020). Method of multi‐region tumour segmentation in brain MRI images using grid‐based segmentation and weighted bee swarm optimisation. IET Image Processing, 14(12), 2901-2910.
Mirfakhraei, S., Abdolvand, N., & Rajaei Harandi, S. (2024). The RFMRv model for customer segmentation based on the referral value. Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies), 17(2), 455-473.
Naami, T., Anesbury, Z. W., Stocchi, L., & Winchester, M. (2022). How websites compete in the Middle East: The example of Iran. Journal of Consumer Behaviour, 21(1), 121-136.
Preethi, P., & Mamatha, H. R. (2023). Region-based convolutional neural network for segmenting text in epigraphical images. Artificial Intelligence and Applications, 1(2), 119-127.
Qin, H., Zhou, H., & Cao, J. (2020). Imbalanced learning algorithm based intelligent abnormal electricity consumption detection. Neurocomputing, 402, 112-123.
Raichura, M., Chothani, N., & Patel, D. (2021). Efficient CNN‐XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Generation, Transmission & Distribution, 15(5), 972-985.
Sagi, O., & Rokach, L. (2021). Approximating XGBoost with an interpretable decision tree. Information sciences, 572, 522-542.
Singh, P. P., Anik, F. I., Senapati, R., Sinha, A., Sakib, N., & Hossain, E. (2024). Investigating customer churn in banking: A machine learning approach and visualization app for data science and management. Data Science and Management, 7(1), 7-16.
Tao, T., Liu, Y., Qiao, Y., Gao, L., Lu, J., Zhang, C., & Wang, Y. (2021). Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm. Renewable Energy, 180, 1004-1013.
Wang, K., Lu, J., Liu, A., Song, Y., Xiong, L., & Zhang, G. (2022). Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. Neurocomputing, 491(6), 288-304.
Xian, H., & Che, J. (2021). A variable weight combined model based on time similarity and particle swarm optimization for short-term power load forecasting. IAENG International Journal of Computer Science, 48(4), 915-924.
Yigit, A. T., Samak, B., & Kaya, T. (2020). An XGBoost-lasso ensemble modeling approach to football player value assessment. Journal of Intelligent & Fuzzy Systems, 39(5), 6303-6314.
Zhao, X., Nie, F., Wang, R., & Li, X. (2022). Improving projected fuzzy K-means clustering via robust learning. Neurocomputing, 491(6), 34-43.
Zhou, B., & Gu, X. (2020). Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection. Neurocomputing, 376(2), 222-231.